In this paper, for the first time, a feature selection procedure is modeled as a multi-criteria
decision making (MCDM) process. This method is applied to a multi-label data and we have …

For the first time, the ensemble feature selection is modeled as a Multi-Criteria Decision-
Making (MCDM) process in this paper. For this purpose, we used the VIKOR method as a …

In multi-label data, each instance corresponds to a set of labels instead of one label
whereby the instances belonging to a label in the corresponding column of that label are …

This paper proposed a Multi-Criteria Decision-Making (MCDM) modeling to deal with multi-
target regression problem. This model offered a feature ranking approach for multi-target …

Many real-world data have multiple class labels known as multi-label data, where the labels
are correlated with each other, and as such, they are not independent. Since these data are …

In multi-label data, each instance is associated with a set of labels, instead of one label.
Similar to single-label data, feature selection plays an important role in improving …

Feature selection is an essential step in data mining and machine learning that increases
classification accuracy and reduces the computational time by eliminating redundant and …

In recent years, there has been a growing interest in multi-label data classification, with a
particular emphasis on multi-label feature selection. While various information-theoretic …

This study introduces an ensemble methodology, namely, hybrid feature ranking and
classifier aggregation (HyFraCa), to integrate ensemble feature selection and ensemble …

High-dimensional multi-label data is widespread in practical applications, which brings great
challenges to the research field of pattern recognition and machine learning. Many feature …